Description of heart-rate variability data in accordance with a physiological model for the genesis of heartbeats.
Description of heart-rate variability data in accordance with a physiological model for the genesis of heartbeats.
复制标题
根据心跳发生的生理模型描述心率变异性数据。
作者：
Roel W. Boer;J. Karemaker;Jan Strackee
介绍了将心率变异性数据转换为既直观又易于分析的信号的技术。引入瞬时心率（IHR）信号，即在相关时间间隔内具有心率（逆搏动间隔）值的信号。IHR信号不同于标准的延迟心率（DHR）信号，后者总是晚一个节拍。讨论了不同表示方法与积分脉冲频率调制（IPFM）模型之间的关系，后者是将连续输入信号（例如，对心脏起搏器的神经影响）转换为一系列事件（心跳）的生理上合理的模型。结果表明，当IHR信号作为IPFM模型的输入时，导出该信号的事件序列出现在输出端。因此，如果IPFM模型被接受为起搏器的模型，IHR信号可能被认为是神经（交感神经和副交感神经）对起搏器影响的近似。此外，我们还表明，与标准DHR信号相比，IHR信号的出现受触发错误或超搏动的影响较小。结论是，最吸引人的生理事件序列时域表示由IHR信号组成，因为该信号在概念上和计算上都很简单，与IPFM模型一致。
A survey is presented of techniques which transform heart-rate variability data into a signal that is both visually informative and accessible for analysis. The Instantaneous Heart-Rate (IHR) signal is introduced, i.e. the signal having the value of the heart rate (inverse interbeat interval) during the interval concerned. The IHR signal differs from the standard Delayed Heart-Rate (DHR) signal, which is always one beat late. The relationship is discussed between the different representation methods and the Integral Pulse Frequency Modulation (IPFM) model, the latter being a physiologically plausible model for the transformation of a continuous input signal (e.g., nervous influence on the cardiac pacemaker) into a series of events (heartbeats). It is shown that when the IHR signal is used as input of the IPFM model, the event series from which the signal was derived appears at the output. Hence, if the IPFM model is accepted as a model of the pacemaker, the IHR signal may be considered as an approximation of the neural (sympathetic and parasympathetic) influence on the pacemaker. In addition we show that the appearance of the IHR signal is less affected by trigger errors or extrasystoles than the standard DHR signal. It is concluded that the most attractive time-domain representation of physiological event series consists of the IHR signal, because this signal, being conceptually and computationally simple, is consistent with the IPFM model.